Biology subjects

Hassan, M. A. S.

Publications and source records attributed to Hassan, M. A. S..

3 recordsLinked to original sources

bioRxiv2019

Multi Epitope Vaccine Prediction Against Aichi Virus using Immunoinformatic Approach

Aichi virus, AiV is single stranded negative sense RNA genome belonging to the genus Kobuviru, a family of Picornaviridae that causes severe gastroenteritis. There is no treatment or vaccine for it, thus the aim of this study is to design a peptide vaccine using immunoinformatic approaches to analyze the viral Protein VP1 of AiV-1 strain, to determine the conserved region which is further studied to predict all possible epitopes that can be used as a peptide vaccine. A total of 38 Aichi virus VP1 retrieved from NCBI database were aligned to determine the conservancy and to predict the epitopes using IEDB analysis resource. Three epitopes predicted as a peptide vaccine for B cell was (PLPPDT, PPLPTP, and LPPLPTP). For T cell, two epitopes showed high affinity to MHC class I (FSIPYTSPL and TMVSFSIPY) and high coverage against the whole world population. Also, in MHC class II, three epitopes that interact with most frequent MHC class II alleles (FTYIAADLR and YMAEVPVSA) with high coverage in the whole world population. For both MHCI and MHCII the T-cell peptide with the strongest affinity to the worldwide population was FSIPYTSPL.\n\nPeptide vaccine against AiV is powerfully displace the normal produced vaccines based on the experimental biochemistry tools, as it designed to handle with a wide range of mutated strains, which will effectively minimize the frequent outbreaks and their massive economical wastage consequences.

bioinformatics↗

bioRxiv2019

Immunoinformatics Prediction of Epitope Based Peptide Vaccine Against Schistosoma Mansoni Fructose Bisphosphate Aldolase Protein

Schistosoma Mansoni represents an important tropical disease that can cause schistosomiasis mostly in Africa and Middle East with high mortality rates. Moreover, no vaccine against it exists. This study predicts an effective epitope-based vaccine against Fructose 1,6 Bisphosphate Aldolase (FBA) enzyme of Schistosoma Mansoni using immunoinformatics approaches. FBA is important for production of energy required for different schistosome activities and survival. The sequences were retrieved from NCBI and several prediction tests were conducted to analyze possible epitopes for B-cell, T-cell MHC class I and II. Tertiary structure of the most promising epitopes was obtained. Two epitopes showed high binding affinity for B-cells, while four epitopes showed high binding affinity for MHCI and MHCII. The results were promising to formulate a vaccine with more than 99.5% population coverage. We hope that these promising epitopes serves as a preventive measure for the disease in the future and recommend invivo and invitro studies.

bioinformatics↗

bioRxiv2019

Immunoinformatics Prediction of Epitope Based Peptide Vaccine Against Mycobacterium Tuberculosis PPE65 family Protein

IntroductionTuberculosis (TB) is a serious disease with varying rates of mortality and morbidity among infected individuals which estimates for approximately two million deaths/year. The number of deaths could increase by 60% if left untreated. It mainly affects immune-compromised individuals and people of third world, due to poverty, low health standards, and inadequate medical care. It has varying range of manifestations that is affected by the host immune system response, the strain causing the infection, its virulence, and transmissibility.\n\nMaterials and methodsA total of 1750 Mycobacterium Tuberculosis PPE65 family protein strains were retrieved from National Center for Biotechnology Information (NCBI) database on March 2019 and several tools were used for the analysis of the T- and B-cell peptides and homology modelling.\n\nResults and conclusionFour strong epitope candidates had been predicted in this study for having good binding affinity to HLA alleles, good global population coverage percentages. These peptides are YAGPGSGPM, AELDASVAM, GRAFNNFAAPRYGFK and a single B-cell peptide YAGP.\n\nThis study uses immunoinformatics approach for the design of peptide based vaccines for M. tuberculosis. Peptide based vaccines are safer, more stable and less hazardous/allergenic when compared to conventional vaccines. In addition, peptide vaccines are less labouring, time consuming and cost efficient. The only weakness is the need to introduce an adjuvant to increase immunogenic stimulation of the vaccine recipient.

bioinformatics↗